Learning Contours for Automatic Annotations of Mountains Pictures on a Smartphone

In the last few years the ubiquity and computational power of modern smartphones, together with the significant progresses made on wireless broadband technologies, have made Augmented Reality (AR) technically feasible in consumer devices. In this paper we present an AR application for mobile phones to augment pictures of mountainous landscapes with geo-referenced data (e.g. the peaks' names, positions of mountain dews or hiking tracks). Our application is based on a novel approach for image-to-world registration, which exploits different information collected with on-board sensors. First, GPS and inertial sensors are used to compute a rough estimate of device position and orientation, then visual cues are exploited to refine it. Specifically, a new learning-based contour detection method based on Random Ferns is used to extract visible mountain profiles from a picture, which are then aligned to synthetic ones obtained from Digital Elevation Models. This solution guarantees an increased accuracy with respect to previous works based only on sensors or on standard edge detection and filtering algorithms. An experimental evaluation conducted on a large set of manually aligned photographs demonstrates that the proposed registration method is both accurate in reconstructing camera position and orientation, and computationally efficient when implemented on a smartphone.